N-channel hidden Markov models for combined stressed speech classification and recognition

Robust speech recognition systems must address variations due to perceptually induced stress in order to maintain acceptable levels of performance in adverse conditions. One approach for addressing these variations is to utilize front-end stress classification to direct a stress dependent recognition algorithm which separately models each speech production domain. This study proposes a new approach which combines stress classification and speech recognition functions into one algorithm. This is accomplished by generalizing the one-dimensional (1-D) hidden Markov model to an N-channel hidden Markov model (N-channel HMM). Here, each stressed speech production style under consideration is allocated a dimension in the N-channel HMM to model each perceptually induced stress condition. It is shown that this formulation better integrates perceptually induced stress effects for stress independent recognition. This is due to the sub-phoneme (state level) stress classification that is implicitly performed by the algorithm. The proposed N-channel stress independent HMM method is compared to a previously established one-channel stress dependent isolated word recognition system yielding a 73.8% reduction in error rate. In addition, an 82.7% reduction in error rate is observed compared to the common one-channel neutral trained recognition approach